Time filter

Source Type

Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University
Advances in Intelligent and Soft Computing | Year: 2012

Distributed generator (DG) is now commonly used in distribution system to reduce total power loss and to improve the power quality of the network. The major task of connecting DG units is to identify their optimal placement in the system and to evaluate the amount of power to be generated in the DG. By considering this objective, a hybrid technique using Genetic algorithm and Neural-network is proposed in this paper. By placing DGs at optimal locations and by evaluating generating power based on the load requirement, the total power loss in the system can be minimized without affecting the voltage stability of the buses. Due to reduction of total power loss in the system and improvement of bus voltages, the power quality of the system increases. The results show the improved performance of proposed method for different number of DGs connected in the system. © 2012 Springer India Pvt. Ltd.


Reddy S.C.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Laxmi A.J.,University of Hyderabad
2012 IEEE 7th International Conference on Industrial and Information Systems, ICIIS 2012 | Year: 2012

Distributed Generators (DGs) are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss can be reduced and voltage profile of the buses and reliability of the system can be improved. The significant process to decrease the total power loss and to improve the power quality of the system is to identify the optimal number of DGs and their suitable locations in the system. To accomplish the aforementioned process and to evaluate the amount of power to be generated, a new method is proposed using Particle Swarm Optimization. The proposed method is tested for IEEE 30 bus system, by connecting optimal number of DGs in the system. The results showed a considerable reduction in the total power loss in the system and improved voltage profiles of the buses and reliability indices. © 2012 IEEE.


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,University of Hyderabad
Journal of Electrical Engineering | Year: 2013

Integration of renewable energy based distributed generation (DG) units provides potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in power losses and emissions, and reliability improvement. Therefore, the allocation of DG units should be carefully determined with the consideration of different planning incentives. It shows the importance of installing the exact amount of DG in the best suitable location. Studies also show that if the DG units are connected at nonoptimal locations or have non-optimal sizes, the system losses will increase. To accomplish the aforementioned process and to evaluate optimal location of DG and the amount of power to be generated, a new method is proposed using Fuzzy Genetic Algorithm (FGA). In this paper, the distribution load flow is run initially without capacitor placement and then the size and location of capacitor in distribution systems based on GA is determined for power loss minimization and voltage profile and reliability improvement. The proposed method is tested for IEEE 33 bus system, by connecting suitable size of DG, capacitor and DG and capacitor at the optimal location of the system. The results showed a considerable reduction in the total power loss in the system, improved voltage profiles of all the buses and Reliability Indices.


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,University of Hyderabad
Journal of Electrical Engineering | Year: 2013

Distributed generator (DG) is now commonly used in distribution system to reduce total power loss and to improve the power quality and reliability of the network. The major task of connecting DG is to identify their optimal placement in the system and to evaluate the amount of power to be generated by DG. By considering this objective, a hybrid technique using Genetic algorithm and Neural-network is proposed in this paper. By placing DG at optimal location and by evaluating generating power based on the load requirement then the number of generators in the network increases and so that different generator states are possible for a particular load condition. The total power loss in the system can be minimized without affecting the voltage stability of the buses. Reliability is old concept and a new discipline. Reliability is, and always has been, one of the major factors in planning, design, operation a nd maint enance of electric power system. Reliability of an electric supply system has been defined as the probability of providing the user with continuous service of satisfactory quality. Reliability prediction is a method of quantitatively stating what is expected to occur and can be used to indicate the relative merits of alternate design proposals with regard to a predetermined level of performance adequacy. Here considered reliability parameters are Loss of Load probability (LOLP) and Expected Energy Not Supplied (EENS).The LOLP and EENS evaluations are based on peak load consideration with load model is considered as straight line. The probability of load exceeding the generating capacity has also been considered in LOLP evaluation. The proposed method is tested for IEEE 30 bus system, by connecting suitable size of DG at the optimal location of the system. The results showed a considerable reduction in the total power loss in the system, improved voltage profiles of all the buses and Reliability parameters.


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,JNTUH College of Engineering
International Review on Modelling and Simulations | Year: 2012

DGs are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss in the can be reduced and voltage profile of the buses and reliability of the system can be improved. The significant process to decrease the total power loss and to improve the reliability of the system is to identify the optimal number of DGs and their suitable locations in the system. To accomplish the aforementioned process and to evaluate the amount of power to be generated, a new method is proposed using Genetic Algorithm. The reliability parameters considered are EENS and ECOST. The proposed method is tested for IEEE 30 bus system, by connecting optimal number of DGs in the system. The results showed a considerable reduction in the total power loss in the system, stable voltage profiles and improved reliability indices. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.


Vinay Kumar D.,National Institute of Technology Warangal | Ravi Kumar P.,National Institute of Technology Warangal | Kumari M.S.,Christu Jyoti Institute of Technology and science
Procedia Engineering | Year: 2013

Different techniques are being attempted over the years to use low pollution emitting fuels in diesel engines to reduce tail pipe emissions with improved engine efficiency. Especially, Biodiesel fuel, derived from different vegetable oils, animal fat and waste cooking oil has received a great attention in the recent past. Transesterification is a proven simplest process to prepare biodiesel in labs with little infrastructure. Application of thermal barrier coatings (TBC) on the engine components is a seriously perused area of interest with low grade fuels like biodiesel fuels. Artificial neural networks (ANN) are gaining popularity to predict the performance and emissions of diesel engines with fairly accurate results besides the thermodynamic models with considerably less complexity and lower computing time. In the present study, experiments have been conducted on a single cylinder diesel engine whose combustion elements are coated with an experimental thermal barrier coating material made from Lanthanum Zirconate. Biodiesel has been prepared from Pongamia Pinnata oil through transesterification process. A series of experiments are conducted on the engine with and without thermal barrier coating using diesel and biodiesel fuels. Performance and emissions data from the experiments is used to train the network with the load, fuel type and coating being the input layer and the brake specific fuel consumption, brake thermal efficiency, CO, HC and NOx emissions being the output layer. Results showed that the coating of engine components with lanthanum zirconate TBC resulted in improved engine efficiency with reduced emissions. ANN model is tested for its accuracy to predict the performance and emissions of the engine with the R values of 0.99 for both the training and test data with a mean square error of 0.002 and a mean relative error of 6.8% © 2013 The Authors. Published by Elsevier Ltd.


Reddy S.C.,Christu Jyoti Institute of Technology and Science | Saritha G.,Christu Jyoti Institute of Technology and Science | Vikas N.,Christu Jyoti Institute of Technology and Science
Proceeding of the IEEE International Conference on Green Computing, Communication and Electrical Engineering, ICGCCEE 2014 | Year: 2014

With the ever increasing demand for power, the complexity and the cost of transmitting extra high power over larger distances increases. Distributed generation provides a relief for the transmission losses by generating power nearby the load. This paper focuses on the effect of distributed generation on the quality of the system voltages at any concerned location due to various faults occurring on the power system. The location of the DG also has a noticeable effect on the system parameters. This work is also intended to enhance the power quality using techniques to balance the phase voltages. Three phase DG is a 3 phase generator connected to all three phases it supplies both real power and reactive power in order to improve the voltage quality. The Presence of a distributed generator also effects the fault location identification as the voltages and the current at the fault location differ because of the presence of distributed generator, hence proper modification have to be made in the protection system in order to work as designed for. In order to analyze the waveforms simulink/matlab software is used. The result of the project helps us to analyze the quality of power delivered in the presence of distributed generators. There by proper modifications can be done in order that the stability of the systems not affected. © 2014 IEEE.


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,JNTUH College of Engineering
European Journal of Scientific Research | Year: 2012

In order to reduce the power losses and to improve the voltage in the distribution system, distributed generators (DGs) are connected to load bus. To reduce the total power losses in the system, the most important process is to identify the proper location for fixing DGs and amount of power to be generated by those DGs. By considering the above objective, a hybrid technique was proposed which includes genetic algorithm and neural network. In the proposed method, genetic algorithm and neural network identifies the possible locations for fixing DGs and the amount of power generated to be by DG. By fixing DGs at suitable locations and also evaluating generating power based on the load conditions, the total power loss in the system can be reduced and the voltage in the buses can be improved. Thus, due to these two improvements, the power quality of the system improves. The proposed method is tested for different load conditions by connecting one DG, two DGs and three DGs in the system. © EuroJournals Publishing, Inc. 2012.

Loading Christu Jyoti Institute of Technology and Science collaborators
Loading Christu Jyoti Institute of Technology and Science collaborators